Validity between dual-energy x-ray absorptiometry and bioelectrical impedance for segmental fat analysis and a novel...

Accurate body fat distribution assessment is essential for managing cardiovascular disease and metabolic disorders. Although several methods are available for segmental fat analysis, few studies have examined the validity of affordable methods such as Bioelectrical Impedance Analysis (BIA) against the reference method, Dual-Energy X-ray Absorptiometry (DXA). This study aimed to assess the validity of BIA as compared to DXA for segmental fat mass assessment, and to develop anthropometric multivariate regression models that offer a cost-effective alternative for health professionals in clinical and public health settings.

Cross-sectional study that included 264 young adults (161 males, mean age = 23.04 ± 5.61 years; and 103 females, mean age = 22.29 ± 5.98 years). Segmental fat mass was measured using DXA and BIA, and anthropometric measurements were collected following the ISAK protocol.

Significant differences were found between DXA and BIA for segmental fat mass (p < 0.001). Sex significantly influenced the results (p < 0.05), while BMI and hydration status had no significant impacts. The Bland-Altman analysis revealed significant differences (p < 0.001) between BIA and DXA for fat mass in the upper and lower limbs. Trunk fat mass also differed significantly in males and females (p < 0.001), except for the overall sample (p = 0.088). Anthropometric multivariate regression models showed a high predictive accuracy for both females (R²=0.766-0.910; p < 0.001) and males (R²=0.758-0.887; p < 0.001). Key predictors of segmental fat mass included body mass (r = 0.606-0.867; p < 0.001), skinfold thickness (r = 0.688-0.893; p < 0.001), and waist girth (r = 0.883 - 0.810; p < 0.001). Peripheral skinfolds were highly predictive for upper and lower limbs, while waist girth was relevant for trunk fat mass.

DXA and BIA are not interchangeable for segmental fat analysis due to the significant differences observed. However, the anthropometric multivariate regression models developed provide a cost-effective and reliable alternative for predicting segmental fat mass in clinical settings where DXA is unavailable.

Not applicable.